# Treatment Effects in Market Equilibrium

arXiv: https://arxiv.org/abs/2109.11647 · HTML: https://mlsanity.com/paper/2109.11647/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2021.

## Built on by (most cited first, 16 of 16)

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- [Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences](https://mlsanity.com/paper/2407.17910/) (2024)
- [Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference](https://mlsanity.com/paper/2510.07646/) (2025)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Causal Estimation of User Learning in Personalized Systems](https://mlsanity.com/paper/2306.00485/) (2023)
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## Abstract (arXiv)

Policy-relevant treatment effect estimation in a marketplace setting requires taking into account both the direct benefit of the treatment and any spillovers induced by changes to the market equilibrium. The standard way to address these challenges is to evaluate interventions via cluster-randomized experiments, where each cluster corresponds to an isolated market. This approach, however, cannot be used when we only have access to a single market (or a small number of markets). Here, we show how to identify and estimate policy-relevant treatment effects using a unit-level randomized trial run within a single large market. A standard Bernoulli-randomized trial allows consistent estimation of direct effects, and of treatment heterogeneity measures that can be used for welfare-improving targeting. Estimating spillovers - as well as providing confidence intervals for the direct effect - requires estimates of price elasticities, which we provide using an augmented experimental design. Our results rely on all spillovers being mediated via the (observed) prices of a finite number of traded goods, and the market power of any single unit decaying as the market gets large. We illustrate our results using a simulation calibrated to a conditional cash transfer experiment in the Philippines.